Mechanical Engineer, Physical AI

Overview

We're building the data layer for physical intelligence.

Robots and AI systems are learning to operate in the real world, and they're hitting a wall: AI can write code and pass the bar exam, but it doesn't know what a real engineer knows. It can't yet look at a part and see what took you years on the floor to learn. Teaching machines that kind of judgment is one of the hardest open problems in AI — and it can't be solved by ML engineers alone. It takes mechanical engineers.

That's the practice we're building, and we're hiring a mechanical engineer to help build it.

We're a Y Combinator company. We work with many of the world's foundational AI labs. We just closed a seed round led by Race Capital, early backers of Databricks and Groq, with angels from OpenAI, NVIDIA, xAI, and Google DeepMind.

https://race.capital/blog/our-investment-in-vision-lab-the-industrial-data-layer-for-physical-ai

The role

Mechanical Engineer, Physical AI. You join at the ground floor of a new practice and shape how it runs.

Most engineering roles use AI as a tool. This one is different: your engineering judgment becomes the standard that frontier AI is trained and measured against. When a machine eventually understands how parts get made, part of what it knows will trace back to what you decided was good enough.

A sense of the work:

• Be the bar the machines have to clear: review manufacturing-grade engineering data against exacting specifications from frontier AI labs — deep technical judgment, every day
• Define what good means: build the QC methodology for a data type nobody has standardized yet, so tight that any reviewer lands on the same verdict
• Teach your judgment to software: work with our engineers to automate verification — encoding what you see into checks that scale
• Shape the specs: turn what AI labs want their models to learn into requirements a supplier can actually deliver against
• Contribute to research on how AI learns engineering and manufacturing, with collaborators at institutions like MIT and Cambridge — publishable work, your name on it

What we want

Someone who understands manufacturing and mechanical engineering at the toolpath level, not the slide-deck level. You don't need an ML background — your engineering depth is the qualification. Curiosity about AI is.

• Bachelor's or Master's in Mechanical Engineering or a closely related field
• You've designed real parts and seen them made — hands-on with fabrication, assembly, and troubleshooting, not just the CAD seat. Automotive, aerospace, electronics, precision machining: if you've shipped parts in Thailand's manufacturing sector, we want to hear from you
• Deep CAD fluency (SolidWorks, NX, CATIA, or similar) and machine design fundamentals; working command of CAM and CNC machining. You can read G-code and tell whether it actually cuts the part
• R&D instincts: prototyping, experimental testing, and iterating on quantitative feedback
• Rigor and consistency: your reviews land on the same verdict every time, and you can explain why
• You're already playing with AI tools and want to get much closer to how they're built
• Strong English and the communication skills to work with multidisciplinary teams. Beneficial: robotics, mechatronics, or automated-systems exposure; experience leading or mentoring engineers; PLM workflows; project management

Comp and logistics

• Six-figure monthly starting salary (THB), scaled to experience and fit. Equity for the right candidate
• Primarily Bangkok, remote/hybrid
• Some opportunity to travel to our SF HQ or other partner markets